LLM-based System Vulnerability
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An LLM-based System Vulnerability is a software system vulnerability for an LLM-based system that exposes security risks due to the model's input-output processes, model architecture, or its integration with larger software systems.
- Context:
- It can involve weaknesses in Input Validation, where improper validation leads to vulnerabilities such as prompt manipulation or unauthorized system access.
- It can lead to Sensitive Information Disclosure if the model inadvertently reveals confidential data through its outputs, highlighting the risks of uncontrolled information leakage.
- It can result from Insecure Plugin Design, where LLM plugins interact without proper security controls, enabling malicious users to exploit the system.
- It can manifest in Training Data Management, particularly when poor oversight leads to poisoning or the introduction of biases that compromise model integrity.
- It can be exploited by LLM-based System Security Attack.
- It can escalate risks of Model Overreliance, when users trust the LLM's output without appropriate verification, leading to decisions based on hallucinated or inaccurate information.
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- Example(s):
- Prompt Injection Vulnerability (that can be exploited by a Prompt Injection Attack), where an attacker crafts inputs that manipulate the LLM into unintended behaviors, such as exposing internal systems or leaking sensitive data.
- Insecure Output Handling Vulnerability (that can be exploited by Injection Attacks), where unsanitized LLM outputs are fed into backend systems, enabling exploits like cross-site scripting (XSS) or remote code execution.
- Training Data Poisoning Vulnerability (that can be exploited by a Training Data Attack), where attackers compromise the integrity of the training data, introducing biases or inaccuracies that degrade the system's reliability.
- Model Denial of Service Vulnerability (that can be exploited by a Denial of Service Attack), where attackers overload the model with resource-intensive inputs, causing system slowdowns or failure.
- Sensitive Information Disclosure Vulnerability (that can be exploited by an Information Disclosure Attack), where the LLM unintentionally outputs proprietary or confidential data, such as API keys or user credentials.
- Insecure Plugin Design Vulnerability (that can be exploited by a Plugin Exploit), where poorly secured LLM plugins allow attackers to escalate privileges or execute malicious code remotely.
- Model Overreliance Vulnerability (that can be exploited by user overdependence on AI), where users uncritically accept LLM outputs, leading to errors or inappropriate decisions based on hallucinated information.
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- Counter-Example(s):
- Manual System Vulnerabilitys, like buffer overflows or SQL injections, which do not involve machine learning models, are not considered LLM-based vulnerabilities.
- Traditional Antivirus Exploits that target specific files or malware signatures without engaging with AI-driven systems differ from vulnerabilities in LLM-based systems.
- See: Machine Learning Security, Cybersecurity Attack Vectors, AI Governance